Neural Network
Ghasem Ahmadi
Abstract
Accurate weather prediction plays a vital role in many sectors, such as agriculture, disaster preparedness, transportation systems, and urban planning. Traditional meteorological models face challenges in capturing complex atmospheric dynamics, leading to increased reliance on artificial neural networks ...
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Accurate weather prediction plays a vital role in many sectors, such as agriculture, disaster preparedness, transportation systems, and urban planning. Traditional meteorological models face challenges in capturing complex atmospheric dynamics, leading to increased reliance on artificial neural networks (ANNs) for improved forecasting accuracy. ANNs have been widely applied in meteorology due to their ability to model nonlinear relationships and temporal dependencies. Based on the Sinc numerical methods, the modified Sinc neural network (MSNN) has been introduced recently. This model uses the advantages of the Sinc function, such as smoothness and fluctuation, and at the same time improves the ability to model nonlinear dependencies and temporal dynamics in environmental data. This work utilizes the MSNN for time series forecasting where its parameters are adjusted with a discrete-time online Lyapunov-based learning algorithm. Then, it is applied to enhance the weather forecasting. This model is evaluated on datasets containing various meteorological variables. The data used in this article is related to the city of Khorramabad in Iran. The results show that despite its simple structure, MSNN has a high efficiency in weather forecasting.
Neural Network
Mohammad Hossein Zolfagharnasab; Latifeh PourMohammadBagher; Mohammad Bahrani
Abstract
This study introduces a tailored recommendation system aimed at enriching Iran’s tourism sector. Using a hybrid model that combines neural collaborative filtering (NCF) with matrix factorization (MF), our approach leverages both demographic and contextual data of combined tourist-landmark (4177 ...
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This study introduces a tailored recommendation system aimed at enriching Iran’s tourism sector. Using a hybrid model that combines neural collaborative filtering (NCF) with matrix factorization (MF), our approach leverages both demographic and contextual data of combined tourist-landmark (4177 samples) to provide personalized touristic recommendations. Empirical evaluations on the implemented methods show that the hybrid model outperforms factorization techniques, achieving a test F1 score of 0.84, accuracy of 0.90, and a test error reduction from 0.83 to 0.37. Feature vector integration further improved test recall by 17%, underscoring the model's robustness in capturing user-item relationships. Further analysis using t-SNE as well as visual analyses of embedding structures confirm the systems ability to generalize patterns in latent space; thereby, mitigating cold-start problem for new tourists or unvisited landmarks. This study also contributes a structured dataset of Iranian landmarks, user ratings, and supplementary contextual data for fostering future research in culturally specific intelligent recommender systems. For implementation details, refer to the GitHub repository at https://github.com/MsainZn/Collaborative_Filtering_Tourism_Landmarks.
Neural Network
Najmeh Jabbari Diziche
Abstract
Parkinson's disease (PD) is a common neurological disorder that has a significant impact on the elderly population worldwide. This study investigates the use of deep learning models, including VGG16, ResNet50, and a simple CNN, in classifying MRI images to distinguish between Parkinson's patients and ...
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Parkinson's disease (PD) is a common neurological disorder that has a significant impact on the elderly population worldwide. This study investigates the use of deep learning models, including VGG16, ResNet50, and a simple CNN, in classifying MRI images to distinguish between Parkinson's patients and normal subjects. The relevant data includes 610 normal subjects and 221 Parkinson subjects. Using ensemble learning techniques with support vector machine (SVM) as a sub-trainer, our model achieved 96% classification accuracy. Applying various hybrid methods such as majority vote, weighted average, and weighted majority vote on the outputs of base learning models helped us achieve a much more improved performance and reduce variability in classification results. These findings promise progress in the accurate diagnosis of Parkinson's disease using deep learning methods in medical imaging. To confirm the practicality of the attained results of the proposed diagnostic approach, further multicenter studies with larger patient groups are recommended.
